Research Activity including Geographical Ontology Modules for Efficient Semantic Web Reuse

1. Research ActivityincludingGeographical Ontology Modules forEfficient Semantic Web Reuse David George, University of Central Lancashire

2. Research Activities • Semantic Heterogeneity • Structural and Semantic discrepancies in database conceptualisation and development • Data and Information Integration • Federated Databases • Mediators: Global-as-View, Local-as-View • Information Brokering Systems and use of Ontology • Semantic Web and Ontology • Practical interaction with Semantic Web Technologies • Protégé, FaCT++, SWOOP, and Jena API Toolkit

3. Research Activities • Development of Jena-based Java Browser Interface: inc • Reading OWL and querying SPARQL • RDF storage in MySQL • Foundation Ontology: SUMO, DOLCE, CyC, BFO (Snap and Span) • Design Best-Practice: Modularity in Ontology development (Rector, 2003) • Experimentation with small-scale OWL ontologies • Formal Concept Analysis - using Concept Explorer

4. Structural & Semantic Heterogeneity • Abstraction Level Conflicts • generalisation/specialisation/aggregation • Schematic Discrepancies • Objects represented differently • Data, attributes, entity • Entity Definition Conflicts • naming conflicts (synonyms and homonyms) • database identifier conflicts e.g. id# v. name • Data Value Conflicts • temporal Inconsistency (last update) • data representation (integer v. string/precision/scale)

5. Data Integration Global Domain Agreements Knowledge Digital media Visual/Spatial/Temporal Data [Kiosk/Geographic/Flights/Forecasting] Focus – Semantics Domain-specific Information Structured, Semi-structured Text repositories Data Structured DBs, Files Focus – Systems & Communications System Schema Integration Common Data Models Virtual Integration Single Ontologies Multiple ontologies, Inter-ontological Local Task Schemas Federated DBS Federated IS (inc Mediators) Information Brokering 1985 1995

6. Jena Toolkit – OWL interface

7. Ontology Specification: Best Practice Ontology elements can be described as: • Primitives: self-standing entities (objects/forms) e.g. Structure, Process, System, Organisation • Relations: concept-linking properties e.g. XhasFormY, hasRole … • Roles: functions e.g. RailTransportRole and • Definables: dependent concepts defined by combining Primitives, Relations, and Roles: • RailwayBridge≡Bridge⊓ (hasForm∃ Structure⊓ • hasRole∃ RailTransportRole)

8. Formal Concept Analysis • Using Concept Explorer • Examined how Concept Analysis may be useful in identifying Classes and Instances in database tables • Considered structural heterogeneity: • Classes represented by single entity (table) • Classes represented by table joins • Classes as subset of table records • Instances represented by entity, attribute, data (record)

9. Formal Concept Analysis Example: Classes represented by table joins

10. Creating Geographical Ontology Modules forEfficient Semantic Web Reuse

11. Ontology and Integration • Ontology Reuse is a key Integration benefit (Noy and Hafner, 1997 ). • Ontology development still at a stage where little interchange between organisations? • Merger, Alignment and Mapping complexity issues with Integration. • Developer reluctance – easier to re-invent own local ontology than reuse. • Reuse of an external ontology will likely result in descriptive and structural irrelevances. • Smaller component ontology modules –improvised as required – may encourage wider usage/take-up

12. Ontology Integration Possible Ontology [ On ] Objectives • Merger: OA + OB→ OC • Alignment: OA≡ OB≡ OC • Mapping: a virtual integration where OA, OB and OC concepts are semantically related. Methods • 1 and 2 are achieved by rewriting (reformulation). • Original ontologies are subsumed or made consistent (respectively). • 3 is achieved by mappings between concepts of imported ontologies. A, B and C endure autonomously. • Ontology Reuse, in this presentation, refers to 3: Mapping. • (Pinto et al., 1999, Noy and Musen, 1999, de Bruijn et al., 2004, Visser and Tamma, 1999, Kalfoglou and Schorlemmer, 2003, Ding et al., 2002)

13. 1 - “Informal” specific Class Reuse • Using namespace declaration to explicitly specify a single external concept, e.g. <rdf:RDF xmlns="http://www.livewiredg.myby.co.uk/rdf/geo-layers/rail.owl#" xmlns:cyc="http://www.cyc.com/2003/04/01/cyc#" > <owl:Class rdf:about="&cyc;TransportationCompany"/> <owl:Class rdf:ID="RailOperator"> <rdfs:subClassOf rdf:resource="#RailwayComponent"/> <rdfs:subClassOf rdf:resource="&cyc;TransportationCompany"/> </owl:Class> …….. • How would an agent understand the Cyc context of the superclass of “cyc:TransportationCompany”

14. 2 - “Formalised” specific Class Reuse E-Connections • Representation and reasoning with foreign ontologies (Grau et al, 2006) • Allows specific concept linking. Few tools available e.g. SWOOP (OWL Ontology Editor) <rdf:RDF xmlns:global="http://www.livewiredg.myby.co.uk/rdf/geo-layers/global.owl#" xmlns=http://www.owl-ontologies.com/flight.owl# ……..> <owl:Class rdf:about=“&global;Artifact"/> <owl:Class rdf:ID="Helicopter"> <rdfs:subClassOf> <owl:Restriction> <owl:onProperty> <owl:LinkProperty rdf:about="#hasForm"/> </owl:onProperty> <owl:someValuesFrom rdf:resource="&global;Artifact"/> </owl:Restriction> </rdfs:subClassOf> </owl:Class> <owl:LinkProperty rdf:ID="hasForm"> <owl:foreignOntology rdf:resource="&global;"/> <rdfs:domain rdf:resource="#Helicopter"/> <rdfs:range> <owl:foreignClass rdf:about="&global;Artifact"> <owl:foreignOntology rdf:resource="&global; "/> </owl:foreignClass> </rdfs:range> </owl:LinkProperty>

15. 3 - “Modularity” by sub-domain separation • SWOOP permits ontology partitioning (module extraction)

16. 4 - Class reuse by Ontology Import Objective: Map Rail Ontology class “RailOperator” to Cyc Ontology class “TransportationCompany” Action: Import Opencyc into Rail > 6.8MB Effect: Adds: 2843 classes 1256 properties load time 1.5 to 7.5 mins Protégé “out of memory”

17. Alternative Reuse approach? • Consider the way Ontologies conceptualised and developed? • Break down domain ontologies into sub-domains (modules) • Try to achieve disjoint structures – minimise redundancy • Can be demonstrated using Geographical context • Geographical concepts interface with virtually every aspect of daily life and feature prominently in information management systems. • Geographical ontologies offer a logical vehicle, to examine how modules can be specified efficiently and effectively.

18. Ontological Inefficiency • Potential redundancy • Vulnerability to change • How relevant are they? • Ontology Reuse - Imports • E.g. if OTN 1 is imported: what do we see? • Ontology much smaller than Cyc, but still multiple sub-domains • Only for an application that uses ALL concepts 1OTN - Ontology of Transportation Networks (Lorenz et al, 2005)

19. Fixed Classes Variable Classes Ontology Permanence

20. Ontology “Geo-Modules” Transportation Tourism Multi-modal Geo-Modules

21. multimodal single-mode ? Land Transport

22. Transport Interchange • multimodal: road-rail • within a town, service facility

23. M6 M67 A6 Visualising Our Transportation Domain

24. PopulationGroupConcept Road domain endsAt* endsAt* City City startsFrom* startsFrom* Rail Transport Ontology Q: rename LevelCrossing → RoadCrossing? But we don’t do Roads in Rail!

25. PopulationGroupConcept Rail domain endsAt* City startsFrom* Road Transport Ontology Q: reclassify ChannelTunnelTerminal → Road Concept? But we don’t do Rail in Roads!

26. LandTransport Ontology

27. LandTransport: Import Consequences • We would need to import: Road, Rail, PopGroups into LandTransport • For just Road and Rail it results in duplications and redundancy

28. M6 M67 A6 Revisualisation: Transportation Layers

29. How do we develop “Geo-Modules” • Need to “de-integrate” to allow low-cost integration • Aim towards “effectively” disjoint domains • Deliver by removing concept duplication between modules – redundancy • Need to promote/relegate multi or single-context concepts and relations

30. Transportation Domain Layers

31. Modular Ontology: +ve/-ve • Advantages • Small is manageable • Select only required building block modules • Independent therefore less vulnerable to change • Change is isolated to the module and subsuming domain? • Disadvantages • Increased mappings? • Needs to be examined

32. References DE BRUIJN, J., DING, Y., ARROYO, S. & FENSEL, D. (2004) Semantic Information Integration in the COG project [online]. Digital Enterprise Research Institute (DERI), University of Innsbruck. Available from: http://www.cogproject.org/publications/sii-wp.pdf. [Accessed 19 December 2004]. DING, Y., FENSEL, D., KLEIN, M. & OMELAYENKO, B. (2002) The semantic web: yet another hip? Data & Knowledge Engineering,41(2), pp. 205-227. DING, Y. & FOO, S. (2002) Ontology Research and Development: Part 2 - A Review of Ontology mapping and evolving. Journal of Information Science,28(5), pp. 383-396. GRAU, B. C., PARSIA, B. & SIRIN, E. (2006) Combining OWL ontologies using E-Connections. Journal of Web Semantics: Science, Services and Agents on the World Wide Web,4(1), pp. 40-59. KALFOGLOU, Y. & SCHORLEMMER, M. (2003) Ontology mapping: the state of the art. The Knowledge Engineering Review,18(1), pp. 1-31. NOY, N. F. & HAFNER, C. D. (1997) The State of the Art in Ontology Design - A Survey and Comparative Review. AI Magazine,18(3), pp. 53-74. NOY, N. F. & MUSEN, M. A. (1999) SMART: Automated Support for Ontology Merging and Alignment Stanford, MA, Stanford Medical Informatics. Available from: http://www-smi.stanford.edu/pubs/SMI_Reports/SMI-1999-0813.pdf. [Accessed 22 December 2004]. PINTO, H. S., GÓMEZ-PÉREZ, A. & MARTINS, J. P. (1999) Some Issues on Ontology Integration. In: Proceedings of IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5). Stockholm, Sweden, August 2 1999. CEUR-WS, pp. 7.1-7.12. RECTOR, A. L. (2003) Modularisation of domain ontologies implemented in description logics and related formalisms including OWL. In: Proceedings of 2nd International Conference On Knowledge Capture. Sanibel Island, FL, USA, 2003. ACM Press, New York, NY, USA, pp. 121-128. VISSER, P. R. S. & TAMMA, V. A. M. (1999) An Experience with Ontology-based Agent Clustering. In: Proceedings of IJCAI-99 workshop on Ontologies and Problem-Solving Methods (KRR5). Stockholm, Sweden, 2 August 1999. CEUR-WS, pp. 12.1-12.13.